Classification of heart sound based on s-transform and neural network

The skill of cardiac auscultatory is very important to physicians for accurate diagnosis of many heart diseases. However, it needs some training and experience to improve the skills of medical students in recognizing and distinguishing the primary symptoms of cardiac diseases based on the heart sound that heard. This paper presents a method for feature extraction and classification of heart sound signals. The S-Transform (ST) technique is used to extract the features of heart sound. Then, the features were applied as inputs to classifier. The Multilayer Perceptron Network has been used to classify heart sound cases. The performance of the technique has been evaluated using 250 cardiac periods of heart sound recorded from heart sound simulator. The result has shown over 98% correct classification which shows the method used is suitable to classify heart sound cases.